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commcare-export 0.11.6

A command-line tool (and Python library) to extract data from CommCareHQ into a SQL database or Excel workbook

Latest Version: 0.12.0

https://github.com/dimagi/commcare-export

A command-line tool (and Python library) to generate customized exports from the CommCareHQ REST API.

Installation & Quick Start

0a. Install Python and pip. This tool is tested with Python 2.6, 2.7, and 3.3.

0b. Sign up for CommCareHQ at https://www.commcarehq.org/register/user if you have not already.

  1. Install CommCare Export via pip
$ pip install commcare-export

2. Visit the CommCareHQ Exchange and add the Simple CommCare Demo/Tutorial” app to a new project space.

3. Visit the Release Manager, make a build, click the star to release it.

  1. Visit CloudCare and fill out a bunch of forms.

5. Try out the example queries in the examples/ directory, providing your project name on the command line:

$ commcare-export \
     --query examples/demo-registration.xlsx \
     --project YOUR_PROJECT \
     --output-format markdown

$ commcare-export \
     --query examples/demo-registration.json \
     --project YOUR_PROJECT \
     --output-format markdown

$ commcare-export \
     --query examples/demo-deliveries.xlsx \
     --project YOUR_PROJECT \
     --output-format markdown

$ commcare-export \
     --query examples/demo-deliveries.json \
     --project YOUR_PROJECT \
     --output-format markdown

You’ll see the tables printed out. Change to --output-format sql --output URL_TO_YOUR_DB --since DATE to sync all forms submitted since that date.

All examples are present in Excel and also equivalent JSON.

Command-line Usage

The basic usage of the command-line tool is with a saved Excel or JSON query (see how to write these, below)

$ commcare-export --commcare-hq <URL or alias like "local" or "prod"> \
                  --username <username> \
                  --project <project> \
                  --version <api version, defaults to latest known> \
                  --query <excel file, json file, or raw json> \
                  --output-format <csv, xls, xlsx, json, markdown, sql> \
                  --output <file name or SQL database URL>

See commcare-export --help for the full list of options.

There are example query files for the CommCare Demo App (available on the CommCareHq Exchange) in the examples/ directory.

Excel Queries

An excel query is any .xlsx workbook. Each sheet in the workbook represents one table you wish to create. There are two grouping of columns to configure the table:

  • Data Source: Set this to form to export form data, or case for case data.
  • Filter Name / Filter Value: These columns are paired up to filter the input cases or forms.
  • Field: The destination in your SQL database for the value.
  • Source Field: The particular field from the form you wish to extract. This can be any JSON path.

JSON Queries

JSON queries are a described in the table below. You build a JSON object that represents the query you have in mind. A good way to get started is to work from the examples, or you could make an excel query and run the tool with --dump-query to see the resulting JSON query.

Python Library Usage

As a library, the various commcare_export modules make it easy to

  • Interact with the CommCareHQ REST API
  • Execute “Minilinq” queries against the API (a very simple query language, described below)
  • Load and save JSON representations of Minilinq queries
  • Compile Excel configurations to Minilinq queries

To directly access the CommCareHq REST API:

>>> import getpass
>>> from commcare_export.commcare_hq_client import CommCareHqClient
>>> api_client = CommCareHqClient('http://commcarehq.org', project='your_project').authenticated('your_username', getpass.getpass())
>>> forms = api_client.iterate('form', {'app_id': "whatever"})
>>> [ (form['received_on'], form['form.gender']) for form in forms ]

To issue a minilinq query against it, and then print out that query in a JSON serialization:

>>> import getpass
>>> import json
>>> from commcare_export.minilinq import *
>>> from commcare_export.commcare_hq_client import CommCareHqClient
>>> from commcare_export.commcare_minilinq import CommCareHqEnv
>>> from commcare_export.env import BuiltInEnv
>>> api_client = CommCareHqClient('http://commcarehq.org', project='your_project').authenticated('your_username', getpass.getpass())
>>> saved_query = Map(source=Apply(Reference("api_data"), [Literal("form"), Literal({"filter": {"term": {"app_id": "whatever"}}})])
                      body=List([Reference("received_on"), Reference("form.gender")]))

>>> forms = saved_query.eval(BuiltInEnv() | CommCareHqEnv(api_client) | JsonPathEnv())
>>> print json.dumps(saved_query.to_jvalue(), indent=2)

Which will output JSON equivalent to this:

{
  "Map": {
    "source": {
      "Apply": {
        "fn":   {"Ref": "api_data"},
        "args": [
          {"Lit": "form"},
          {"Lit": {"filter": {"term": {"app_id": "something"}}}}
        ]
      }
    },
    "body": {
      "List": [
        {"Ref": "received_on"},
        {"Ref": "form.gender"}
      ]
    }
  }
}

MiniLinq Reference

The abstract syntax can be directly inspected in the commcare_export.minilinq module. Note that the choice between functions and primitives is deliberately chosen to expose the structure of the MiniLinq for possible optimization, and to restrict the overall language.

Here is a description of the astract syntax and semantics

Python JSON Which is evaluates to
Literal(v) {"Lit": v} Just v
Reference(x) {"Ref": x} Whatever x resolves to in the environment
List([a, b, c, ...]) {"List": [a, b, c, ...} The list of what a, b, c evaluate to
Map(source, name, body) {"Map": {"source": ..., "name": ..., "body": ...} Evals body for each elem in source. If name is provided, the elem will be bound to it, otherwise it will replace the whole env.
FlatMap(source, name, body) {"FlatMap": {"source" ... etc}} Flattens after mapping, like nested list comprehensions
Filter(source, name, body) etc  
Bind(value, name, body) etc Binds the result of value to name when evaluating body
Emit(table, headings, rows) etc Emits table with headings and rows. Note that table is a string, headings is a list of expressions, and rows is a list of lists of expressions. See explanation belowe for emitted output.
Apply(fn, args) etc Evaluates fn to a function, and all of args, then applies the function to the args.

Built in functions like api_data and basic arithmetic and comparison are provided via the environment, referred to be name using Ref, and utilized via Apply

Output Formats

Your MiniLinq may define multiple tables with headings in addition to their body rows by using Emit expressions, or may simply return the results of a single query.

If your MiniLinq does not contain any Emit expressions, then the results of the expression will be printed to standard output as pretty-printed JSON.

If your MiniLinq does contain Emit expressions, then there are many formats available, selected via the --output-format <format> option, and it can be directed to a file with the --output <file> command-line option.

  • csv: Each table will be a CSV file within a Zip archive.
  • xls: Each table will be a sheet in an old-format Excel spreadsheet.
  • xlsx: Each table will be a sheet in a new-format Excel spreadsheet.
  • json: The tables will each be a member of a JSON dictionary, printed to standard output
  • markdown: The tables will be streamed to standard output in Markdown format (very handy for debugging your queries)
  • sql: All data will be idempotently “upserted” into the SQL database you specify, including creating the needed tables and columns.

Dependencies

Required dependencies will be automatically installed via pip. But since you may not care about all export formats, the various dependencies there are optional. Here is how you might install them:

# To export "xlsx"
$ pip install openpyxl

# To export "xls"
$ pip install xlwt

# To sync with a SQL database
$ pip install SQLAlchemy alembic

Contributing

  1. Sign up for github, if you have not already, at https://github.com.
  2. Fork the repository at https://github.com/dimagi/commcare-export.

2. Clone your fork, install into a virtualenv, and start a feature branch

$ mkvirtualenv commcare-export
$ git clone git@github.com:YOUR_USERNAME/commcare-export.git
$ cd commcare-export
$ pip install -e .
$ git checkout -b my-super-duper-feature
  1. Make your edits.

4. Make sure the tests pass. The best way to test for all versions is to sign up for https://travis-ci.org and turn on automatic continuous testing for your fork.

$ py.test
=============== test session starts ===============
platform darwin -- Python 2.7.3 -- pytest-2.3.4
collected 17 items

tests/test_commcare_minilinq.py .
tests/test_excel_query.py ....
tests/test_minilinq.py ........
tests/test_repeatable_iterator.py .
tests/test_writers.py ...

============ 17 passed in 2.09 seconds ============
  1. Push the feature branch up
$ git push -u origin my-super-duper-feature

6. Visit https://github.com/YOUR_USERNAME/commcare-export and submit a pull request.

  1. Accept our gratitude for contributing: Thanks!
 
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commcare-export-0.11.6.tar.gz (md5) Source 2014-04-10 22KB
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